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Review
. 2022 Jun 9:2022:2042736.
doi: 10.1155/2022/2042736. eCollection 2022.

Diagnostic Performance of Diffusion Kurtosis Imaging for Benign and Malignant Breast Lesions: A Systematic Review and Meta-Analysis

Hongyu Gu #  1 , Wenjing Cui #  2 , Song Luo  3 , Xiaoyi Deng  1
Affiliations
Review

Diagnostic Performance of Diffusion Kurtosis Imaging for Benign and Malignant Breast Lesions: A Systematic Review and Meta-Analysis

Hongyu Gu et al. Appl Bionics Biomech. .

Retraction in

Abstract

Purpose: Magnetic resonance imaging (MRI) has a high sensitivity for differentiating between malignant and non-malignant breast lesions but is sometimes limited due to its low specificity. Here, we performed a meta-analysis to evaluate the diagnostic performance of mean kurtosis (MK) and mean diffusivity (MD) values in magnetic resonance diffusion kurtosis imaging (DKI) for benign and malignant breast lesions.

Methods: Original articles on relevant topics, published from 2010 to 2019, in PubMed, EMBASE, and WanFang databases were systematically reviewed. According to the purpose of the study and the characteristics of DKI reported, the diagnostic performances of MK and MD were evaluated, and meta-regression was conducted to explore the source of heterogeneity.

Results: Fourteen studies involving 1,099 (451 benign and 648 malignant) lesions were analyzed. The pooled sensitivity, pooled specificity, positive likelihood ratio, and negative likelihood ratio for MD were 0.84 (95% confidence interval (CI), 0.81-0.87), 0.83 (95% CI, 0.79-0.86), 4.44 (95% CI, 3.54-5.57), and 0.18 (95% CI, 0.13-0.26), while those for MK were 0.89 (95% CI, 0.86-0.91), 0.86 (95% CI, 0.82-0.89), 5.72 (95% CI, 4.26-7.69), and 0.13 (95% CI, 0.09-0.19), respectively. The overall area under the curve (AUC) was 0.91 for MD and 0.95 for MK.

Conclusions: Analysis of the data from 14 studies showed that MK had a higher pooled sensitivity, pooled specificity, and diagnostic performance for differentiating between breast lesions, compared with MD.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Flow diagram of study selection for meta-analysis.
Figure 2
Figure 2
Quality assessment of the included studies using the QUADAS-2 tool.
Figure 3
Figure 3
Forest plots of pooled sensitivity, pooled specificity, positive LR, and negative LR for MD in detecting benign and malignant breast lesions. LR: likelihood ratio; MD: mean diffusivity.
Figure 4
Figure 4
The SROC curve, publication bias, and Fagan Nomogram for MD in detecting benign and malignant breast lesions. SROC: summary receiver operating characteristic; MD: mean diffusivity.
Figure 5
Figure 5
Forest plots of pooled sensitivity, pooled specificity, positive LR, and negative LR for MK in detecting benign and malignant breast lesions. LR: likelihood ratio; MK: mean kurtosis.
Figure 6
Figure 6
The SROC curve, publication bias, and Fagan Nomogram for MK in detecting benign and malignant breast lesions. SROC: summary receiver operating characteristic; MK: mean kurtosis.
See this image and copyright information in PMC

References

    1. DeMartini W., Lehman C. A review of current evidence-based clinical applications for breast magnetic resonance imaging. Topics in Magnetic Resonance Imaging . 2008;19(3):143–150. doi: 10.1097/RMR.0b013e31818a40a5. - DOI - PubMed
    1. Gao F., Wu T., Li J., et al. SD-CNN: a shallow-deep CNN for improved breast cancer diagnosis. Computerized Medical Imaging and Graphics . 2018;70:53–62. doi: 10.1016/j.compmedimag.2018.09.004. - DOI - PubMed
    1. Ye D. M., Wang H. T., Yu T. The application of radiomics in breast MRI: a review. Technology in Cancer Research & Treatment . 2020;19, article 1533033820916191 - PMC - PubMed
    1. Zhang J., Li L., Zhe X., et al. The diagnostic performance of machine learning-based radiomics of DCE-MRI in predicting axillary lymph node metastasis in breast cancer: a meta-analysis. Frontiers in Oncology . 2022;12:p. 799209. doi: 10.3389/fonc.2022.799209. - DOI - PMC - PubMed
    1. Jafar M. M., Parsai A., Miquel M. E. Diffusion-weighted magnetic resonance imaging in cancer: reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility. World Journal of Radiology . 2016;8(1):21–49. doi: 10.4329/wjr.v8.i1.21. - DOI - PMC - PubMed

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